Today, many personal electronic devices come equipped with digital cameras. Often, these devices perform many functions, and, as a consequence, the digital image sensors included in these devices must often be smaller than the sensors in conventional cameras. Further, the camera hardware in these devices often have smaller dynamic ranges and lack sophisticated features sometimes found in larger, professional-style conventional cameras such as manual exposure controls and manual focus. Thus, it is important that digital cameras in personal electronic devices be able to produce visually appealing images in a wide variety of lighting and scene situations with limited or no interaction from the user, as well as in a computationally and cost effective manner.
One feature that has been implemented in some digital cameras to compensate for lack of dynamic range and create visually appealing images is known as “auto exposure.” Auto exposure (AE) can be defined generally as any algorithm that automatically calculates and/or manipulates certain camera exposure parameters, e.g., exposure time, ISO, gain, or f-number, in such a way that the currently exposed scene is captured in a desirable manner. For example, there may be a predetermined optimum brightness value for a given scene that the camera will try to achieve by adjusting the camera's exposure value. Exposure value (EV) can be defined generally as: log2 N2/t, wherein N is the relative aperture (f-number), and t is the exposure time (i.e., “shutter speed”) expressed in seconds. Some auto exposure algorithms calculate and/or manipulate the exposure parameters such that a mean, center-weighted mean, median, or more complicated weighted value (as in matrix-metering) of the image's brightness will equal a predetermined optimum brightness value in the resultant, auto exposed scene.
Auto exposure algorithms are often employed in conjunction with image sensors having small dynamic ranges because the dynamic range of light in a given scene, i.e., from absolute darkness to bright sunlight, is much larger than the range of light that some image sensors—such as those often found in personal electronic devices—are capable of capturing. In much the same way that the human brain can drive the diameter of the eye's pupil to let in a desired amount of light, an auto exposure algorithm can drive the exposure parameters of a camera so as to attempt to most effectively capture the desired portions of a scene. The difficulties associated with image sensors having small dynamic ranges are further exacerbated by the fact that most image sensors in personal electronic devices are comparatively smaller than those in larger cameras, resulting in a smaller number of photons that can hit any single photosensor of the image sensor.
Auto exposure algorithms work to drive exposure parameters, thus, it is problematic when such auto exposure algorithms are “tricked” by the composition of a particular scene. For example, with an auto exposure algorithm using a center-weighted mean to expose an outdoor scene that is brightly lit by the sun but has a dark object at its center, the auto exposure algorithm may be “tricked” by large numbers of heavily-weighted, dark pixels near the center of the image, and thus act to change the camera's exposure parameter values, e.g., by lengthening the exposure time—incorrectly assuming (based on the image's predominantly dark center) that it is looking at an overall dark image that would benefit from an increased exposure time. However, lengthening the exposure time could have the unintended consequence of over—exposing the bright areas around the peripheral parts of the scene, potentially causing “blowouts” to occur, that is, areas in the image where pixel brightness exceeds the sensor's dynamic range of capturing capability, thus losing all image detail information in those areas of the image and producing only pure white pixels.
Thus, there is need for systems, methods, and a computer readable medium for performing an improved auto exposure blowout prevention process implemented in an image capture device or video capture device, e.g., a camera circuit in a digital camera, mobile phone, personal data assistant (PDA), portable music player, or laptop/desktop/tablet computer, to detect and compensate for occurrences of exposure blowouts caused by auto exposure algorithms that have been “tricked” by the composition of a particular scene. Additionally, such techniques may be able to distinguish between properly-captured specular highlights, for example, the glint or shine on a pair of eyeglasses (which should not be corrected for) and blowouts caused by an auto exposure algorithm overexposing a scene more than was necessary.